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5. Applied Spatial Data Analysis with R

R is a statistical programming environment with good support forreproducible research. The first chunk will motivate the use of R withspatial data, and introduce "sp" classes and methods used to representspatial data, as well as showing links to PROJ.4, GDAL and GEOS.

Handling spatial data in R - GRASS interface

The "sp" classes can be used to exchange data with GIS, here GRASS is usedas an example. GRASS can also be scripted using R without moving data toR.

Handling spatial data in R - methods

It is often useful to examine and analyse geometries in a structured way,including intersections, areas, and overlays. A number of alternatives areexplored.

Worked examples in spatial statistics

As R is a statistical environment, the worked examples will be taken fromspatial statistics, in particular disease mapping.

Some knowledge of R will be an advantage, but knowledge of otherinterpreted languages will help if R is new.

Hands-on

Participants are going to learn: WCS/WCPS standards; setting up and querying spatio-temporal coverages within those; rasdaman and its components for input/output.

A virtual environment will be provided for the hands-on session; alternatively participants can also come with a working laptop installation. For guidance and support see our wiki and mailing-list (rasdaman-users@googlegroups.com).